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Forest_cal_sender.py
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Forest_cal_sender.py
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from LEARNTOBRANCH import LEARNTOBRANCH
import paddle.nn as nn
import paddle
import paddle.nn.initializer as init
from coder import *
"""
一个返回sender输出的森林结构
"""
class ForestNet(LEARNTOBRANCH):
def __init__(self, dataset, num_attributes, num_channels=64):
super(ForestNet, self).__init__()
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
fc_channel = 1
self.dataset = dataset
if dataset == 'CIFAR10':
self.num_children = [1, 2, 4, 8, 8]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
# self.ds = [level1, level2, level3, level4]
self.output_channels = [num_attributes] * 8
self.classifier_nodes = [[1], [2], [4, 4], [8, 8, 8, 8]]
self.fc_num = 8
elif dataset == 'CIFAR100':
self.fc_num = 8
self.num_children = [1, 2, 4, 8, 8]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
self.output_channels = [num_attributes] * self.fc_num
self.classifier_nodes = [[1], [2], [4, 4], [8] * 4]
elif dataset == 'TINY-IMAGENET':
self.fc_num = 8
self.num_children = [1, 2, 4, 8, 8]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
self.output_channels = [num_attributes] * self.fc_num
self.classifier_nodes = [[1], [2], [4, 4], [8] * 4]
self.branches = []
for layer in range(len(self.num_children) - 1):
layer_child = self.num_children[layer]
for i in range(layer_child): # block
setattr(self, 'conv{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Conv2D(self.num_in_channels[layer], self.num_out_channels[layer], kernel_size=3,
padding=1),
nn.BatchNorm2D(self.num_out_channels[layer]),
nn.ReLU(True),
nn.Conv2D(self.num_out_channels[layer], self.num_in_channels[layer + 1], kernel_size=3,
padding=1),
nn.BatchNorm2D(self.num_in_channels[layer + 1]),
nn.ReLU(True),
nn.MaxPool2D(kernel_size=2)]))
if layer != len(self.num_children) - 2 and self.classifier_nodes[layer + 1][i] > 1:
setattr(self, 'router{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Conv2D(self.num_in_channels[layer + 1], num_channels // 2, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2D(num_channels // 2, num_channels // 2, kernel_size=3, padding=1),
nn.ReLU(True),
nn.AdaptiveAvgPool2D((3, 3))
]))
setattr(self, 'router_classifier{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Linear(9 * num_channels // 2, self.classifier_nodes[layer + 1][i]),
nn.Softmax()
]))
if layer < len(self.num_children) - 2:
setattr(self, 'branch_{}'.format(str(layer + 2)),
nn.ParameterList([paddle.create_parameter(
shape=[self.num_children[layer + 1], self.num_children[layer]], dtype="float32",
default_initializer=init.Uniform(low=0, high=1))]))
self.branches.append(getattr(self, 'branch_{}'.format(str(layer + 2))))
for i in range(self.fc_num):
if self.output_channels[i] > 1:
setattr(self, 'fc1_' + str(i),
nn.Sequential(*[
nn.Linear(fc_channel * num_channels, self.output_channels[i]),
nn.Softmax()
]))
self.num_attributes = num_attributes
self._initialize_weights()
def forward(self, x, t=10, training=True):
bs = x.shape[0]
xs = [] # store the output from previous layer
x_branches = [x] # next level input
pro = paddle.ones([bs, 1]).cuda()
pre_pro = [pro]
pros = []
pros_return = dict()
for layer in range(len(self.num_children) - 1):
layer_child = self.num_children[layer]
for i in range(layer_child): # block
conv = getattr(self, 'conv{}_{}'.format(str(layer + 2), str(i)))
# print(layer, i)
after_conv = conv(x_branches[i])
xs.append(after_conv)
if layer != len(self.num_children) - 2:
if self.classifier_nodes[layer + 1][i] > 1:
router = getattr(self, 'router{}_{}'.format(str(layer + 2), str(i)))
classifier = getattr(self, 'router_classifier{}_{}'.format(str(layer + 2), str(i)))
pro = router(after_conv)
pro = paddle.reshape(pro, [bs, -1])
pro = classifier(pro)
pro = paddle.clip(pro, 0.001, 0.999) # paddle.clip==pytorch.clamp
# pro = (pro >= torch.max(pro, 1)[0].view(bs, -1)).float() * pro
pro = pro * (paddle.reshape(pre_pro[i], [bs, -1]))
pro = paddle.clip(pro, 0.001, 0.999)
pros.append(pro)
else:
pros.append(paddle.ones([bs, 1]).cuda() * (paddle.reshape(pre_pro[i], [bs, -1])))
pros_return['pro{}_{}'.format(str(layer + 2), str(i))] = pro
if layer != len(self.num_children) - 2:
x_branches = []
pre_pro = []
d = self.branching_op(self.branches[layer], layer_child, self.num_children[layer + 1], t, training)
indexes = [0] * layer_child
for i in range(self.num_children[layer + 1]): # child
pro = 0
x_branch = 0
for j in range(layer_child): # par
if j == 0:
x_branch = xs[j] * d[i][j] # xs[j]是上一层的输出
else:
x_branch += xs[j] * d[i][j]
if layer != len(self.num_children) - 2:
pro += pros[j][:, indexes[j]]
indexes[j] += 1
x_branches.append(x_branch) # 为每一个下一层的lerner,生成一个输入
if layer != len(self.num_children) - 2:
pre_pro.append(pro)
xs = []
pros = []
outputs = 0
for i in range(self.fc_num):
tx = xs[i]
pro = paddle.reshape(pre_pro[i], [bs, -1])
tx = self.avgpool(tx)
pro = paddle.clip(pro, 0.001, 0.999)
# outputs+=pro
tx = paddle.reshape(tx, [paddle.shape(x)[0], -1])
if self.output_channels[i] > 1:
fc1 = getattr(self, 'fc1_' + str(i))
out = fc1(tx)
out = paddle.clip(out, 0.001, 0.999)
pro = out * pro
# pro = torch.clamp(pro, 0.001, 0.999)
outputs += pro
# print(torch.sum(outputs, 1))
outputs = paddle.log(outputs)
return outputs, pros_return